calcCounts: Calculate cluster cell counts

In lmweber/diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering

Calculate cluster cell counts

Description

Calculate number of cells per cluster-sample combination

Usage

calcCounts(d_se)

Arguments

d_se	Data object from previous steps, in SummarizedExperiment format, containing cluster labels as a column in the row meta-data (from generateClusters).

Details

Calculate number of cells per cluster-sample combination (referred to as cluster cell 'counts', 'abundances', or 'frequencies').

The cluster cell counts are required for testing for differential abundance of cell populations, and are also used for weights and filtering when testing for differential states within cell populations.

Results are returned as a new SummarizedExperiment object, where rows = clusters, columns = samples, assay = values (counts). (Note that this structure differs from the input data object.)

Value

d_counts: SummarizedExperiment object, where rows = clusters, columns = samples, assay = values (counts).

Examples

# For a complete workflow example demonstrating each step in the 'diffcyt' pipeline, 
# see the package vignette.

# Function to create random data (one sample)
d_random <- function(n = 20000, mean = 0, sd = 1, ncol = 20, cofactor = 5) {
  d <- sinh(matrix(rnorm(n, mean, sd), ncol = ncol)) * cofactor
  colnames(d) <- paste0("marker", sprintf("%02d", 1:ncol))
  d
}

# Create random data (without differential signal)
set.seed(123)
d_input <- list(
  sample1 = d_random(), 
  sample2 = d_random(), 
  sample3 = d_random(), 
  sample4 = d_random()
)

experiment_info <- data.frame(
  sample_id = factor(paste0("sample", 1:4)), 
  group_id = factor(c("group1", "group1", "group2", "group2")), 
  stringsAsFactors = FALSE
)

marker_info <- data.frame(
  channel_name = paste0("channel", sprintf("%03d", 1:20)), 
  marker_name = paste0("marker", sprintf("%02d", 1:20)), 
  marker_class = factor(c(rep("type", 10), rep("state", 10)), 
                        levels = c("type", "state", "none")), 
  stringsAsFactors = FALSE
)

# Prepare data
d_se <- prepareData(d_input, experiment_info, marker_info)

# Transform data
d_se <- transformData(d_se)

# Generate clusters
d_se <- generateClusters(d_se)

# Calculate counts
d_counts <- calcCounts(d_se)

lmweber/diffcyt documentation built on March 19, 2024, 5:24 a.m.